In order to enhance image contrast and dynamic range, the histogram equalization algorithm is usually used for image processing, wherein the histogram equalization algorithm mainly has two implementations: one is the global histogram equalization algorithm, i.e., an image to be processed is holistically adjusted by an equilibrium mapping curve and a mapping restriction parameter that relates to information abundance of the image, in such a way to not only avoid image deterioration while enhancing contrast, but also reduce calculation amount as much as possible; however, in order to prevent such an issue as the short board effect, the details of the processed image usually cannot be enhanced most properly; and the other is the local histogram equalization algorithm, i.e., an image is divided into several areas, the image to be processed in each area is adjusted by an equilibrium mapping curve and a mapping restriction parameter that relates to information abundance of the area, and the equilibrium mapping curve in each area is decided by its surrounding areas. The greatest advantage of this method is that the details of an image can be enhanced most properly; however, it tends to result in a huge amount of calculation for perfectly highlighting the details.